Overview

Dataset statistics

Number of variables23
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory227.5 MiB
Average record size in memory184.0 B

Variable types

Numeric10
DateTime2
Text8
Categorical3

Alerts

Unnamed: 0 is highly overall correlated with unix_timeHigh correlation
lat is highly overall correlated with merch_latHigh correlation
long is highly overall correlated with merch_long and 1 other fieldsHigh correlation
merch_lat is highly overall correlated with latHigh correlation
merch_long is highly overall correlated with long and 1 other fieldsHigh correlation
unix_time is highly overall correlated with Unnamed: 0High correlation
zip is highly overall correlated with long and 1 other fieldsHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
trans_num has unique valuesUnique

Reproduction

Analysis started2024-07-12 11:53:37.216101
Analysis finished2024-07-12 11:55:26.918036
Duration1 minute and 49.7 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1296675
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean648337
Minimum0
Maximum1296674
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:26.986927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64833.7
Q1324168.5
median648337
Q3972505.5
95-th percentile1231840.3
Maximum1296674
Range1296674
Interquartile range (IQR)648337

Descriptive statistics

Standard deviation374317.97
Coefficient of variation (CV)0.57735094
Kurtosis-1.2
Mean648337
Median Absolute Deviation (MAD)324169
Skewness-5.1691189 × 10-15
Sum8.4068238 × 1011
Variance1.4011395 × 1011
MonotonicityStrictly increasing
2024-07-12T17:40:27.064420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
864447 1
 
< 0.1%
864454 1
 
< 0.1%
864453 1
 
< 0.1%
864452 1
 
< 0.1%
864451 1
 
< 0.1%
864450 1
 
< 0.1%
864449 1
 
< 0.1%
864448 1
 
< 0.1%
864446 1
 
< 0.1%
Other values (1296665) 1296665
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
1296674 1
< 0.1%
1296673 1
< 0.1%
1296672 1
< 0.1%
1296671 1
< 0.1%
1296670 1
< 0.1%
1296669 1
< 0.1%
1296668 1
< 0.1%
1296667 1
< 0.1%
1296666 1
< 0.1%
1296665 1
< 0.1%
Distinct1274791
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum2019-01-01 00:00:18
Maximum2020-06-21 12:13:37
2024-07-12T17:40:27.134968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:27.204860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cc_num
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1719204 × 1017
Minimum6.0416207 × 1010
Maximum4.9923464 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:27.273668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6.0416207 × 1010
5-th percentile6.3048488 × 1011
Q11.8004295 × 1014
median3.5214173 × 1015
Q34.6422555 × 1015
95-th percentile4.497914 × 1018
Maximum4.9923464 × 1018
Range4.9923463 × 1018
Interquartile range (IQR)4.4622125 × 1015

Descriptive statistics

Standard deviation1.3088064 × 1018
Coefficient of variation (CV)3.1371798
Kurtosis6.1799499
Mean4.1719204 × 1017
Median Absolute Deviation (MAD)3.0764709 × 1015
Skewness2.851879
Sum-6.7255419 × 1018
Variance1.7129743 × 1036
MonotonicityNot monotonic
2024-07-12T17:40:27.348014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.713652351 × 10113123
 
0.2%
4.512828415 × 10183123
 
0.2%
3.672269902 × 10133119
 
0.2%
2.131124026 × 10143117
 
0.2%
3.54510934 × 10153113
 
0.2%
6.534628261 × 10153112
 
0.2%
6.011367958 × 10153110
 
0.2%
2.720433096 × 10153107
 
0.2%
6.011438889 × 10153106
 
0.2%
6.011109737 × 10153101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
6.041620718 × 10101518
0.1%
6.042292873 × 10101531
0.1%
6.042309813 × 1010510
 
< 0.1%
6.042785159 × 1010528
 
< 0.1%
6.048700208 × 1010496
 
< 0.1%
6.049059630 × 10101010
0.1%
6.049559311 × 1010518
 
< 0.1%
5.018029536 × 10111559
0.1%
5.018181333 × 10118
 
< 0.1%
5.018282048 × 1011515
 
< 0.1%
ValueCountFrequency (%)
4.992346398 × 10182059
0.2%
4.989847571 × 10181007
 
0.1%
4.980323468 × 1018532
 
< 0.1%
4.973530368 × 10181040
0.1%
4.958589672 × 10181476
0.1%
4.95682899 × 10182566
0.2%
4.911818931 × 10189
 
< 0.1%
4.906628656 × 10182584
0.2%
4.897067971 × 10181038
0.1%
4.890424427 × 10181496
0.1%
Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:27.487246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.132597
Min length13

Characters and Unicode

Total characters29995460
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Rippin, Kub and Mann
2nd rowfraud_Heller, Gutmann and Zieme
3rd rowfraud_Lind-Buckridge
4th rowfraud_Kutch, Hermiston and Farrell
5th rowfraud_Keeling-Crist
ValueCountFrequency (%)
and 474111
 
15.7%
llc 97780
 
3.2%
inc 91939
 
3.0%
sons 73145
 
2.4%
ltd 70853
 
2.3%
plc 66475
 
2.2%
group 50447
 
1.7%
fraud_kutch 10560
 
0.3%
fraud_schaefer 9394
 
0.3%
fraud_streich 9250
 
0.3%
Other values (804) 2069403
68.4%
2024-07-12T17:40:27.729068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29995460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29995460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29995460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
gas_transport
131659 
grocery_pos
123638 
home
123115 
shopping_pos
116672 
kids_pets
113035 
Other values (9)
688556 

Length

Max length14
Median length12
Mean length10.526079
Min length4

Characters and Unicode

Total characters13648903
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_pos
3rd rowentertainment
4th rowgas_transport
5th rowmisc_pos

Common Values

ValueCountFrequency (%)
gas_transport 131659
10.2%
grocery_pos 123638
9.5%
home 123115
9.5%
shopping_pos 116672
9.0%
kids_pets 113035
8.7%
shopping_net 97543
7.5%
entertainment 94014
7.3%
food_dining 91461
 
7.1%
personal_care 90758
 
7.0%
health_fitness 85879
 
6.6%
Other values (4) 228901
17.7%

Length

2024-07-12T17:40:27.819735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport 131659
10.2%
grocery_pos 123638
9.5%
home 123115
9.5%
shopping_pos 116672
9.0%
kids_pets 113035
8.7%
shopping_net 97543
7.5%
entertainment 94014
7.3%
food_dining 91461
 
7.1%
personal_care 90758
 
7.0%
health_fitness 85879
 
6.6%
Other values (4) 228901
17.7%

Most occurring characters

ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13648903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13648903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13648903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

amt
Real number (ℝ)

SKEWED 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:27.886419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2024-07-12T17:40:27.957514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 542
 
< 0.1%
1.04 538
 
< 0.1%
1.25 535
 
< 0.1%
1.02 533
 
< 0.1%
1.01 523
 
< 0.1%
1.05 519
 
< 0.1%
1.2 516
 
< 0.1%
1.23 515
 
< 0.1%
1.08 512
 
< 0.1%
1.11 509
 
< 0.1%
Other values (52918) 1291433
99.6%
ValueCountFrequency (%)
1 222
< 0.1%
1.01 523
< 0.1%
1.02 533
< 0.1%
1.03 499
< 0.1%
1.04 538
< 0.1%
1.05 519
< 0.1%
1.06 471
< 0.1%
1.07 498
< 0.1%
1.08 512
< 0.1%
1.09 496
< 0.1%
ValueCountFrequency (%)
28948.9 1
< 0.1%
27390.12 1
< 0.1%
27119.77 1
< 0.1%
26544.12 1
< 0.1%
25086.94 1
< 0.1%
17897.24 1
< 0.1%
15305.95 1
< 0.1%
15047.03 1
< 0.1%
15034.18 1
< 0.1%
14849.74 1
< 0.1%

first
Text

Distinct352
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:28.137445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.0804319
Min length3

Characters and Unicode

Total characters7884344
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJennifer
2nd rowStephanie
3rd rowEdward
4th rowJeremy
5th rowTyler
ValueCountFrequency (%)
christopher 26669
 
2.1%
robert 21667
 
1.7%
jessica 20581
 
1.6%
james 20039
 
1.5%
michael 20009
 
1.5%
david 19965
 
1.5%
jennifer 16940
 
1.3%
william 16371
 
1.3%
mary 16346
 
1.3%
john 16325
 
1.3%
Other values (342) 1101763
85.0%
2024-07-12T17:40:28.408851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7884344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7884344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7884344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

last
Text

Distinct481
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:28.580125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1111774
Min length2

Characters and Unicode

Total characters7924211
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBanks
2nd rowGill
3rd rowSanchez
4th rowWhite
5th rowGarcia
ValueCountFrequency (%)
smith 28794
 
2.2%
williams 23605
 
1.8%
davis 21910
 
1.7%
johnson 20034
 
1.5%
rodriguez 17394
 
1.3%
martinez 14805
 
1.1%
jones 13976
 
1.1%
lewis 12753
 
1.0%
gonzalez 11799
 
0.9%
miller 11698
 
0.9%
Other values (471) 1119907
86.4%
2024-07-12T17:40:28.812515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7924211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7924211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7924211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
F
709863 
M
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Length

2024-07-12T17:40:28.898221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T17:40:28.956757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
f 709863
54.7%
m 586812
45.3%

Most occurring characters

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1296675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1296675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1296675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

street
Text

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:29.089106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.229027
Min length12

Characters and Unicode

Total characters28823823
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row561 Perry Cove
2nd row43039 Riley Greens Suite 393
3rd row594 White Dale Suite 530
4th row9443 Cynthia Court Apt. 038
5th row408 Bradley Rest
ValueCountFrequency (%)
apt 327791
 
6.4%
suite 305467
 
5.9%
island 22954
 
0.4%
michael 18967
 
0.4%
common 17978
 
0.3%
station 17957
 
0.3%
islands 17917
 
0.3%
david 17476
 
0.3%
brooks 16991
 
0.3%
fields 16321
 
0.3%
Other values (1940) 4376722
84.9%
2024-07-12T17:40:29.323017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28823823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28823823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28823823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

city
Text

Distinct894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:29.512627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.6522459
Min length3

Characters and Unicode

Total characters11219151
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoravian Falls
2nd rowOrient
3rd rowMalad City
4th rowBoulder
5th rowDoe Hill
ValueCountFrequency (%)
city 21314
 
1.3%
west 19473
 
1.2%
north 14425
 
0.9%
saint 14363
 
0.9%
falls 12794
 
0.8%
new 11842
 
0.7%
mount 11375
 
0.7%
lake 11249
 
0.7%
san 10260
 
0.6%
springs 8727
 
0.5%
Other values (918) 1482445
91.6%
2024-07-12T17:40:29.767351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11219151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11219151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11219151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:29.888270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2593350
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowWA
3rd rowID
4th rowMT
5th rowVA
ValueCountFrequency (%)
tx 94876
 
7.3%
ny 83501
 
6.4%
pa 79847
 
6.2%
ca 56360
 
4.3%
oh 46480
 
3.6%
mi 46154
 
3.6%
il 43252
 
3.3%
fl 42671
 
3.3%
al 40989
 
3.2%
mo 38403
 
3.0%
Other values (41) 724142
55.8%
2024-07-12T17:40:30.059160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2593350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2593350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2593350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

zip
Real number (ℝ)

HIGH CORRELATION 

Distinct970
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48800.671
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:30.147563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126237
median48174
Q372042
95-th percentile94569
Maximum99783
Range98526
Interquartile range (IQR)45805

Descriptive statistics

Standard deviation26893.222
Coefficient of variation (CV)0.55108305
Kurtosis-1.0964493
Mean48800.671
Median Absolute Deviation (MAD)23068
Skewness0.079680758
Sum6.327861 × 1010
Variance7.2324542 × 108
MonotonicityNot monotonic
2024-07-12T17:40:30.222906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73754 3646
 
0.3%
34112 3613
 
0.3%
48088 3597
 
0.3%
82514 3527
 
0.3%
49628 3123
 
0.2%
15484 3123
 
0.2%
85173 3119
 
0.2%
29819 3117
 
0.2%
38761 3113
 
0.2%
5461 3112
 
0.2%
Other values (960) 1263585
97.4%
ValueCountFrequency (%)
1257 2023
0.2%
1330 1031
 
0.1%
1535 515
 
< 0.1%
1545 1024
 
0.1%
1612 519
 
< 0.1%
1843 2597
0.2%
1844 2058
0.2%
2180 519
 
< 0.1%
2630 2090
0.2%
2908 550
 
< 0.1%
ValueCountFrequency (%)
99783 1568
0.1%
99747 12
 
< 0.1%
99746 540
 
< 0.1%
99323 2572
0.2%
99160 3030
0.2%
99116 15
 
< 0.1%
99113 1047
 
0.1%
99033 2458
0.2%
98836 524
 
< 0.1%
98665 500
 
< 0.1%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537622
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:30.300675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6205
median39.3543
Q341.9404
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3199

Descriptive statistics

Standard deviation5.0758084
Coefficient of variation (CV)0.13171047
Kurtosis0.81296795
Mean38.537622
Median Absolute Deviation (MAD)3.3597
Skewness-0.18602768
Sum49970771
Variance25.763831
MonotonicityNot monotonic
2024-07-12T17:40:30.379348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.385 3646
 
0.3%
26.1184 3613
 
0.3%
42.5164 3597
 
0.3%
43.0048 3527
 
0.3%
39.8936 3123
 
0.2%
44.5995 3123
 
0.2%
33.2887 3119
 
0.2%
34.0326 3117
 
0.2%
33.4783 3113
 
0.2%
44.3346 3112
 
0.2%
Other values (958) 1263585
97.4%
ValueCountFrequency (%)
20.0271 1527
0.1%
20.0827 1032
 
0.1%
24.6557 2584
0.2%
26.1184 3613
0.3%
26.3304 542
 
< 0.1%
26.3771 518
 
< 0.1%
26.4215 3038
0.2%
26.4722 2524
0.2%
26.529 1549
0.1%
26.6939 1027
 
0.1%
ValueCountFrequency (%)
66.6933 12
 
< 0.1%
65.6899 540
 
< 0.1%
64.7556 1568
0.1%
48.8878 3030
0.2%
48.8856 2066
0.2%
48.8328 1533
0.1%
48.6669 1047
 
0.1%
48.6031 2973
0.2%
48.4786 2038
0.2%
48.34 3088
0.2%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct969
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226335
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2024-07-12T17:40:30.450361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.158
95-th percentile-73.5112
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.64

Descriptive statistics

Standard deviation13.759077
Coefficient of variation (CV)-0.15249513
Kurtosis1.8558923
Mean-90.226335
Median Absolute Deviation (MAD)8.1527
Skewness-1.1501077
Sum-1.1699423 × 108
Variance189.3122
MonotonicityNot monotonic
2024-07-12T17:40:30.523808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.0727 3646
 
0.3%
-81.7361 3613
 
0.3%
-82.9832 3597
 
0.3%
-108.8964 3527
 
0.3%
-79.7856 3123
 
0.2%
-86.2141 3123
 
0.2%
-111.0985 3119
 
0.2%
-82.2027 3117
 
0.2%
-90.5142 3113
 
0.2%
-73.098 3112
 
0.2%
Other values (959) 1263585
97.4%
ValueCountFrequency (%)
-165.6723 1568
0.1%
-156.292 540
 
< 0.1%
-155.488 1032
0.1%
-155.3697 1527
0.1%
-153.994 12
 
< 0.1%
-124.4409 1043
0.1%
-124.2174 1547
0.1%
-124.1587 1031
0.1%
-124.1437 1526
0.1%
-123.9743 2036
0.2%
ValueCountFrequency (%)
-67.9503 2080
0.2%
-68.5565 1014
 
0.1%
-69.2675 519
 
< 0.1%
-69.4828 2050
0.2%
-69.9576 537
 
< 0.1%
-69.9656 3107
0.2%
-70.1031 9
 
< 0.1%
-70.239 1036
 
0.1%
-70.3001 2090
0.2%
-70.3457 1527
0.1%

city_pop
Real number (ℝ)

Distinct879
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88824.441
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:30.594334image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation301956.36
Coefficient of variation (CV)3.3994738
Kurtosis37.614519
Mean88824.441
Median Absolute Deviation (MAD)2198
Skewness5.5938531
Sum1.1517643 × 1011
Variance9.1177644 × 1010
MonotonicityNot monotonic
2024-07-12T17:40:30.667690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606 5496
 
0.4%
1595797 5130
 
0.4%
1312922 5075
 
0.4%
1766 4574
 
0.4%
241 4533
 
0.3%
2906700 4168
 
0.3%
276002 4155
 
0.3%
302 4147
 
0.3%
910148 4073
 
0.3%
198 4067
 
0.3%
Other values (869) 1251257
96.5%
ValueCountFrequency (%)
23 2049
0.2%
37 1013
 
0.1%
43 2034
0.2%
46 3040
0.2%
47 511
 
< 0.1%
49 1054
 
0.1%
51 1016
 
0.1%
52 518
 
< 0.1%
53 2610
0.2%
60 1045
 
0.1%
ValueCountFrequency (%)
2906700 4168
0.3%
2504700 2033
 
0.2%
2383912 521
 
< 0.1%
1595797 5130
0.4%
1577385 2563
0.2%
1526206 3517
0.3%
1417793 8
 
< 0.1%
1382480 2056
0.2%
1312922 5075
0.4%
1263321 3629
0.3%

job
Text

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:30.797242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.227102
Min length3

Characters and Unicode

Total characters26227978
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist
ValueCountFrequency (%)
engineer 131756
 
4.6%
officer 110915
 
3.9%
manager 61124
 
2.1%
scientist 55878
 
1.9%
designer 52218
 
1.8%
surveyor 49062
 
1.7%
teacher 38126
 
1.3%
psychologist 32600
 
1.1%
research 29754
 
1.0%
editor 28725
 
1.0%
Other values (456) 2289024
79.5%
2024-07-12T17:40:31.024589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26227978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26227978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26227978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

dob
Date

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum1924-10-30 00:00:00
Maximum2005-01-29 00:00:00
2024-07-12T17:40:31.118543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:31.195591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

trans_num
Text

UNIQUE 

Distinct1296675
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:31.785014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters41493600
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1296675 ?
Unique (%)100.0%

Sample

1st row0b242abb623afc578575680df30655b9
2nd row1f76529f8574734946361c461b024d99
3rd rowa1a22d70485983eac12b5b88dad1cf95
4th row6b849c168bdad6f867558c3793159a81
5th rowa41d7549acf90789359a9aa5346dcb46
ValueCountFrequency (%)
0b242abb623afc578575680df30655b9 1
 
< 0.1%
c1d9a7ddb1e34639fe82758de97f4abf 1
 
< 0.1%
189a841a0a8ba03058526bcfe566aab5 1
 
< 0.1%
83ec1cc84142af6e2acf10c44949e720 1
 
< 0.1%
6d294ed2cc447d2c71c7171a3d54967c 1
 
< 0.1%
fc28024ce480f8ef21a32d64c93a29f5 1
 
< 0.1%
7bb25a43205191eb7344282b88fc54d3 1
 
< 0.1%
3b9014ea8fb80bd65de0b1463b00b00e 1
 
< 0.1%
3c74776e558f1499a7824b556e474b1d 1
 
< 0.1%
413636e759663f264aae1819a4d4f231 1
 
< 0.1%
Other values (1296665) 1296665
> 99.9%
2024-07-12T17:40:32.388779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2596593
 
6.3%
9 2596375
 
6.3%
7 2595084
 
6.3%
4 2594676
 
6.3%
a 2594103
 
6.3%
d 2593816
 
6.3%
3 2593713
 
6.3%
f 2593666
 
6.3%
5 2593098
 
6.2%
e 2592759
 
6.2%
Other values (6) 15549717
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41493600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2596593
 
6.3%
9 2596375
 
6.3%
7 2595084
 
6.3%
4 2594676
 
6.3%
a 2594103
 
6.3%
d 2593816
 
6.3%
3 2593713
 
6.3%
f 2593666
 
6.3%
5 2593098
 
6.2%
e 2592759
 
6.2%
Other values (6) 15549717
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41493600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2596593
 
6.3%
9 2596375
 
6.3%
7 2595084
 
6.3%
4 2594676
 
6.3%
a 2594103
 
6.3%
d 2593816
 
6.3%
3 2593713
 
6.3%
f 2593666
 
6.3%
5 2593098
 
6.2%
e 2592759
 
6.2%
Other values (6) 15549717
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41493600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2596593
 
6.3%
9 2596375
 
6.3%
7 2595084
 
6.3%
4 2594676
 
6.3%
a 2594103
 
6.3%
d 2593816
 
6.3%
3 2593713
 
6.3%
f 2593666
 
6.3%
5 2593098
 
6.2%
e 2592759
 
6.2%
Other values (6) 15549717
37.5%

unix_time
Real number (ℝ)

HIGH CORRELATION 

Distinct1274823
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3492436 × 109
Minimum1.325376 × 109
Maximum1.3718168 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:32.495531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.328672 × 109
Q11.3387507 × 109
median1.3492497 × 109
Q31.3593854 × 109
95-th percentile1.3698306 × 109
Maximum1.3718168 × 109
Range46440799
Interquartile range (IQR)20634633

Descriptive statistics

Standard deviation12841278
Coefficient of variation (CV)0.0095173904
Kurtosis-1.0875405
Mean1.3492436 × 109
Median Absolute Deviation (MAD)10358807
Skewness0.0033779498
Sum1.7495305 × 1015
Variance1.6489843 × 1014
MonotonicityIncreasing
2024-07-12T17:40:32.573436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1370177227 4
 
< 0.1%
1335110521 4
 
< 0.1%
1370050667 4
 
< 0.1%
1367602155 3
 
< 0.1%
1364686521 3
 
< 0.1%
1369587838 3
 
< 0.1%
1337306743 3
 
< 0.1%
1343668520 3
 
< 0.1%
1341944714 3
 
< 0.1%
1340650327 3
 
< 0.1%
Other values (1274813) 1296642
> 99.9%
ValueCountFrequency (%)
1325376018 1
< 0.1%
1325376044 1
< 0.1%
1325376051 1
< 0.1%
1325376076 1
< 0.1%
1325376186 1
< 0.1%
1325376248 1
< 0.1%
1325376282 1
< 0.1%
1325376308 1
< 0.1%
1325376318 1
< 0.1%
1325376361 1
< 0.1%
ValueCountFrequency (%)
1371816817 1
< 0.1%
1371816816 1
< 0.1%
1371816752 1
< 0.1%
1371816739 1
< 0.1%
1371816728 1
< 0.1%
1371816696 1
< 0.1%
1371816683 1
< 0.1%
1371816656 1
< 0.1%
1371816562 1
< 0.1%
1371816522 1
< 0.1%

merch_lat
Real number (ℝ)

HIGH CORRELATION 

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-07-12T17:40:32.652429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2024-07-12T17:40:32.726578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.305966 4
 
< 0.1%
41.937796 4
 
< 0.1%
42.265012 4
 
< 0.1%
41.301611 4
 
< 0.1%
34.134994 4
 
< 0.1%
37.669788 4
 
< 0.1%
39.348185 4
 
< 0.1%
32.64469 4
 
< 0.1%
42.749184 4
 
< 0.1%
38.050673 4
 
< 0.1%
Other values (1247795) 1296635
> 99.9%
ValueCountFrequency (%)
19.027785 1
< 0.1%
19.027804 1
< 0.1%
19.029798 1
< 0.1%
19.031242 1
< 0.1%
19.032277 1
< 0.1%
19.033288 1
< 0.1%
19.034282 1
< 0.1%
19.034687 1
< 0.1%
19.035472 1
< 0.1%
19.036312 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.682905 1
< 0.1%
66.67355 1
< 0.1%
66.664673 1
< 0.1%
66.659242 1
< 0.1%

merch_long
Real number (ℝ)

HIGH CORRELATION 

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2024-07-12T17:40:32.803244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2024-07-12T17:40:32.877634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.116414 4
 
< 0.1%
-81.219189 4
 
< 0.1%
-74.618269 4
 
< 0.1%
-85.326323 3
 
< 0.1%
-84.890305 3
 
< 0.1%
-88.49309 3
 
< 0.1%
-84.100102 3
 
< 0.1%
-97.527227 3
 
< 0.1%
-85.3444 3
 
< 0.1%
-86.037494 3
 
< 0.1%
Other values (1275735) 1296642
> 99.9%
ValueCountFrequency (%)
-166.671242 1
< 0.1%
-166.670132 1
< 0.1%
-166.669638 1
< 0.1%
-166.666179 1
< 0.1%
-166.664828 1
< 0.1%
-166.662888 1
< 0.1%
-166.661968 1
< 0.1%
-166.659277 1
< 0.1%
-166.657834 1
< 0.1%
-166.657174 1
< 0.1%
ValueCountFrequency (%)
-66.950902 1
< 0.1%
-66.955996 1
< 0.1%
-66.95654 1
< 0.1%
-66.958659 1
< 0.1%
-66.958751 1
< 0.1%
-66.959178 1
< 0.1%
-66.961923 1
< 0.1%
-66.962913 1
< 0.1%
-66.963918 1
< 0.1%
-66.963975 1
< 0.1%

is_fraud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Length

2024-07-12T17:40:32.943317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T17:40:32.994485image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1296675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1296675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1296675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Interactions

2024-07-12T17:40:19.659220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:07.813469image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.284205image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.566863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.861280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:13.248617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.579940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.869082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.112391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:18.328713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:19.794890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:08.050574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.406721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.700732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.992088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:13.385845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.710115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.998095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.236368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:18.468098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:19.921613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:08.194329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.534742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.817645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:12.121777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:13.519339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.838497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.124607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.357030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:18.610366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:20.056274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:08.327232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.657605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.970058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:12.250846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:13.650520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.967975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.250461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.479084image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:18.742379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:20.187027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:08.466439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.786031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.109773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:12.383322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:13.779360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.100992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.374821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.599586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:18.875225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:20.316382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:08.635010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.909033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.243880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:12.512833image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:13.909264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.225351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.499306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.719524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:19.006308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:20.442283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:08.768700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.026975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.367283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:12.657626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.037962image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.352938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.617919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.839277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:19.139438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:20.578468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:08.898140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.147377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.491099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:12.818485image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.187317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.490496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.746970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:17.957494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:19.273030image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:20.717529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.038675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.266215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.612477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:12.964874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.319624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.622657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.872504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:18.077630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:19.403120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:20.848816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:09.162352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:10.400092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:11.728587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:13.114659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:14.445570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:15.751896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:16.991705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:18.198159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-12T17:40:19.531780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-07-12T17:40:33.038714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Unnamed: 0amtcategorycc_numcity_popgenderis_fraudlatlongmerch_latmerch_longunix_timezip
Unnamed: 01.0000.0010.0010.002-0.0030.0000.0190.001-0.0010.001-0.0011.0000.001
amt0.0011.0000.020-0.001-0.0240.0000.0000.012-0.0000.0120.0000.0010.001
category0.0010.0201.0000.0060.0210.0540.071-0.009-0.000-0.009-0.0000.0000.002
cc_num0.002-0.0010.0061.0000.0490.0510.006-0.004-0.013-0.004-0.0130.0020.013
city_pop-0.003-0.0240.0210.0491.0000.0890.004-0.2650.087-0.2640.086-0.003-0.040
gender0.0000.0000.0540.0510.0891.0000.0080.0500.0590.0500.059-0.001-0.065
is_fraud0.0190.0000.0710.0060.0040.0081.0000.0010.0030.0010.003-0.005-0.002
lat0.0010.012-0.009-0.004-0.2650.0500.0011.0000.1060.9910.1050.001-0.162
long-0.001-0.000-0.000-0.0130.0870.0590.0030.1061.0000.1060.998-0.001-0.959
merch_lat0.0010.012-0.009-0.004-0.2640.0500.0010.9910.1061.0000.1040.001-0.162
merch_long-0.0010.000-0.000-0.0130.0860.0590.0030.1050.9980.1041.000-0.001-0.957
unix_time1.0000.0010.0000.002-0.003-0.001-0.0050.001-0.0010.001-0.0011.0000.001
zip0.0010.0010.0020.013-0.040-0.065-0.002-0.162-0.959-0.162-0.9570.0011.000

Missing values

2024-07-12T17:40:21.254294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-12T17:40:22.570272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0trans_date_trans_timecc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraud
002019-01-01 00:00:182703186189652095fraud_Rippin, Kub and Mannmisc_net4.97JenniferBanksF561 Perry CoveMoravian FallsNC2865436.0788-81.17813495Psychologist, counselling1988-03-090b242abb623afc578575680df30655b9132537601836.011293-82.0483150
112019-01-01 00:00:44630423337322fraud_Heller, Gutmann and Ziemegrocery_pos107.23StephanieGillF43039 Riley Greens Suite 393OrientWA9916048.8878-118.2105149Special educational needs teacher1978-06-211f76529f8574734946361c461b024d99132537604449.159047-118.1864620
222019-01-01 00:00:5138859492057661fraud_Lind-Buckridgeentertainment220.11EdwardSanchezM594 White Dale Suite 530Malad CityID8325242.1808-112.26204154Nature conservation officer1962-01-19a1a22d70485983eac12b5b88dad1cf95132537605143.150704-112.1544810
332019-01-01 00:01:163534093764340240fraud_Kutch, Hermiston and Farrellgas_transport45.00JeremyWhiteM9443 Cynthia Court Apt. 038BoulderMT5963246.2306-112.11381939Patent attorney1967-01-126b849c168bdad6f867558c3793159a81132537607647.034331-112.5610710
442019-01-01 00:03:06375534208663984fraud_Keeling-Cristmisc_pos41.96TylerGarciaM408 Bradley RestDoe HillVA2443338.4207-79.462999Dance movement psychotherapist1986-03-28a41d7549acf90789359a9aa5346dcb46132537618638.674999-78.6324590
552019-01-01 00:04:084767265376804500fraud_Stroman, Hudson and Erdmangas_transport94.63JenniferConnerF4655 David IslandDublinPA1891740.3750-75.20452158Transport planner1961-06-19189a841a0a8ba03058526bcfe566aab5132537624840.653382-76.1526670
662019-01-01 00:04:4230074693890476fraud_Rowe-Vandervortgrocery_net44.54KelseyRichardsF889 Sarah Station Suite 624HolcombKS6785137.9931-100.98932691Arboriculturist1993-08-1683ec1cc84142af6e2acf10c44949e720132537628237.162705-100.1533700
772019-01-01 00:05:086011360759745864fraud_Corwin-Collinsgas_transport71.65StevenWilliamsM231 Flores Pass Suite 720EdinburgVA2282438.8432-78.60036018Designer, multimedia1947-08-216d294ed2cc447d2c71c7171a3d54967c132537630838.948089-78.5402960
882019-01-01 00:05:184922710831011201fraud_Herzog Ltdmisc_pos4.27HeatherChaseF6888 Hicks Stream Suite 954ManorPA1566540.3359-79.66071472Public affairs consultant1941-03-07fc28024ce480f8ef21a32d64c93a29f5132537631840.351813-79.9581460
992019-01-01 00:06:012720830304681674fraud_Schoen, Kuphal and Nitzschegrocery_pos198.39MelissaAguilarF21326 Taylor Squares Suite 708ClarksvilleTN3704036.5220-87.3490151785Pathologist1974-03-283b9014ea8fb80bd65de0b1463b00b00e132537636137.179198-87.4853810
Unnamed: 0trans_date_trans_timecc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraud
129666512966652020-06-21 12:08:42213193596103206fraud_Gulgowski LLChome72.17JamesHuntM7369 Gabriel TunnelPointe Aux PinsMI4977545.7549-84.447095Electrical engineer1994-02-09108c103b26f686c24c021aaf4210977e137181652244.938461-83.9962340
129666612966662020-06-21 12:09:224587657402165341815fraud_Hyatt, Russel and Gleichnerhealth_fitness7.30AmberLewisF6296 John Keys Suite 858Pembroke TownshipIL6095841.0646-87.59172135Psychotherapist, child2004-05-0837a18c6fb0c5c722b6339ffedc82f55a137181656240.556811-88.0923390
129666712966672020-06-21 12:10:564822367783500458fraud_Hahn, Douglas and Schowaltertravel19.71ChristopherFarrellM97070 Anderson LandHaines CityFL3384428.0758-81.592933804Exercise physiologist1991-01-0134e72e0a659a6c8f4a20ee65594f3a7d137181665627.465871-81.5118040
129666812966682020-06-21 12:11:23213141712584544fraud_Metz, Russel and Metzkids_pets100.85MargaretCurtisF742 Oneill ShoreFlorenceMS3907332.1530-90.121719685Fine artist1984-12-240d86d8c17638d7eff77db9c6a878b477137181668331.377697-90.5284500
129666912966692020-06-21 12:11:364400011257587661852fraud_Stiedemann Incmisc_pos37.38MarissaPowellF474 Allen HavenNorth LoupNE6885941.4972-98.7858509Nurse, children's1980-09-159a7ea2625cf8303efe34e3c09546868f137181669641.728638-99.0396600
129667012966702020-06-21 12:12:0830263540414123fraud_Reichel Incentertainment15.56ErikPattersonM162 Jessica Row Apt. 072HatchUT8473537.7175-112.4777258Geoscientist1961-11-24440b587732da4dc1a6395aba5fb41669137181672836.841266-111.6907650
129667112966712020-06-21 12:12:196011149206456997fraud_Abernathy and Sonsfood_dining51.70JeffreyWhiteM8617 Holmes Terrace Suite 651TuscaroraMD2179039.2667-77.5101100Production assistant, television1979-12-11278000d2e0d2277d1de2f890067dcc0a137181673938.906881-78.2465280
129667212966722020-06-21 12:12:323514865930894695fraud_Stiedemann Ltdfood_dining105.93ChristopherCastanedaM1632 Cohen Drive Suite 639High Rolls Mountain ParkNM8832532.9396-105.8189899Naval architect1967-08-30483f52fe67fabef353d552c1e662974c137181675233.619513-105.1305290
129667312966732020-06-21 12:13:362720012583106919fraud_Reinger, Weissnat and Strosinfood_dining74.90JosephMurrayM42933 Ryan UnderpassMandersonSD5775643.3526-102.54111126Volunteer coordinator1980-08-18d667cdcbadaaed3da3f4020e83591c83137181681642.788940-103.2411600
129667412966742020-06-21 12:13:374292902571056973207fraud_Langosh, Wintheiser and Hyattfood_dining4.30JeffreySmithM135 Joseph MountainsSulaMT5987145.8433-113.8748218Therapist, horticultural1995-08-168f7c8e4ab7f25875d753b422917c98c9137181681746.565983-114.1861100